Australia Consumers are most satisfied with the post-fraud service of banks and insurances companies

More than 70% satisfaction rate compared to 59.7% on average

Impact: Increased trust in BFSIs

Observations

BFSI have the most superior capabilities in fraud management–in speed of detection, accuracy, and contextualization.

Indonesia Consumers that encountered most fraud incidents in the past 12 months

49%

34.7%

AP Average

49.8% have experienced fraud at least once compared to 34.7% on average

Impact: Overall anti-fraud capabilities need improvement

Observations

Spurt in digital activity in the recent past has initiated investment in new technologies (i.e., robotics, AI, machine learning, biometrics) for better fraud detection and improved contextual marketing. The industry is working in collaboration with third-party agencies on alternative data sources, and is open to work with fintech companies.

Singapore Consumers have the highest trust towards government

AP Average

75.5% choose government agencies, compared with 51.7% on average

Impact: Trust of personal data protection is centered around government agencies

Observations

BFSI seeks to increase consumer trust by investing in data quality and data management issues, and in new technologies (e.g., AI, biometrics).

Vietnam Consumers encountered most fraud incidents in retail and telco during the past 12 months

55%

TELCO

54.5%

RETAIL

AP Average

32.8%

TELCO

35.2%

RETAIL

55% and 54.5% have experienced fraud at least once in retail and telco, respectively, compared to 32.8% and 35.2% on average

Impact: Overall anti-fraud capabilities need improvement

Observations

The rapid deployment of fiber to the home and LTE mobile broadband is accelerating the need for customer analytics and collections and fraud management by telcos; retailers are most concerned about employee fraud, because ofiine is a larger sales channel.

Impact: Response time as one of key factors to fraud management to retain customers and gain their trust

Observations

Companies are generally weak in fraud management, but could be inﬂuenced by customers being more tolerant of fraud.

India as standalone Consumers have the largest number of shopping app accounts in the region

Average of three accounts per person

Impact: Highest exposure to online fraud

Observations

Spurt in digital activity in the recent past has initiated investment in new technologies (i.e., robotics, AI, machine learning, biometrics) for better fraud detection and improved contextual marketing. The industry is working in collaboration with third-party agencies on alternative data sources, and is open to work with fintech companies.

Majority of companies realize that they have moderate capabilities in fraud detection.

China Consumers are the most tolerant toward submitting and sharing of personal data

AP Average

46.6% compared to the AP average of 27.5% are accepting of sharing personal data of existing accounts with other business entities

Impact: higher exposure of data privacy and risk of fraud

Observations

Companies have moderate capabilities in fraud detection, but majority believe that improvements are required in areas of fraud resolution for better assistance to victims of fraud as consumers increasingly face more fraud risks.

Japan as standalone Consumers most cautious on digital accounts and transactions

50.7%Actively maintain digital accounts’ validity

27%AP Average

45.5%Do not do online bank transfers

13.5%AP Average

More than 70% did not encounter fraud incidents in past 12 months, compared to 50% on average

Impact: Relatively low risk of fraud

Observations

Spurt in digital activity in the recent past has initiated investment in new technologies (i.e., robotics, AI, machine learning, biometrics) for better fraud detection and improved contextual marketing. The industry is working in collaboration with third-party agencies on alternative data sources, and is open to work with fintech companies.

Nov 20 2018 /

Experian

Unlocking the Power of Data for a Better Future – Insights From Singapore Fintech Festival 2018

James Coffey, Head of Channels and Alliances, South East Asia, and Gaurav Kumar, Head of Data Labs, Asia Pacific speaking on “Embedded Machine Learning in the New Age Products” at the Singapore Fintech Festival 2018

However, as the world digitalises, how can these companies boost their ability to process even more amounts of data, and what are some of the use cases of big data beyond consumer insights?

These were among the topics addressed by Experian’s James Coffey, Head of Channels and Alliances, South East Asia, and Gaurav Kumar, Head of Data Labs, Asia Pacific at the 2018 Singapore Fintech Festival, the world’s largest fintech event.

Speaking to a packed room, James and Gaurav shared insights on how emerging technologies such as Machine Learning and Artificial Intelligence could help organisations boost their data analysis capabilities. The session also covered details on how data-driven solutions created at Experian are making a difference in the lives of consumers in Asia Pacific.

Innovative Answers for Complex Problems

The sheer amount of data modern organisations face each day can be challenging. This is an obstacle that Experian’s DataLabs, a research facility with offices all around the world, is working tirelessly to overcome with technology. The DataLabs aids in the creation of innovative solutions characterised by a unique blend of best in class data, advanced analytics and digital decision strategies, allowing for more effective management of big data scenarios.

Experian also collaborates with start-ups, lending its technology and resources to enable complementary firms to scale more efficiently or deliver better services. Aside from these partnerships, Experian is formulating a Venture Program focused on growth stage minority investment opportunities and a ‘service for equity’ program designed to give start-ups access to Experian services in exchange for equity.

Machine Learning – Why it Matters for Big Data

While it might be daunting to think that Machine Learning provides computers with the ability to learn without being explicitly programmed to do so, when implemented correctly, the automation enabled by Machine Learning is a highly effective tool that vastly enhances the capability of human teams to process massive amounts of data. At Experian, we believe in the potential to harness this technology to make a positive difference in the lives of billions of consumers across Asia Pacific.

Data processing models augmented with Machine Learning capabilities have proven to have strong predictive power, being able to process non-standard data sources for more optimal results.

Use cases benefitting from this include fraud detection, delivery of personalised and targeted product recommendations, and risk score analysis on a national and regional scale. On average, Machine Learning has delivered 20 per cent more accuracy in preventing fraud for Experian. In addition, the combination of different models that Experian deploys has reduced false positives significantly: 80 per cent of valid transactions that traditional models reject as fraud, are successfully passed, increasing customer satisfaction and business outcomes.

In the near term, Experian foresees a growing adoption of Machine Learning augmented data processing models in the near term as organisations deal with an increasingly large number of data-sets and digital touchpoints that necessitate more efficient ways of processing data rapidly.

Empowering Consumers with Data Driven Financial Access

Experian has long recognised the transformational potential of data, harnessing its power to create opportunities, improve lives, and make a difference to society. A key focus for Experian revolves around the use of data to drive financial inclusion in regions such as Southeast Asia, where only 48 per cent of more than 600 million people have access to traditional banking services.

Experian’s approach to enhancing financial accessibility is centred around the use of non-traditional data for credit scoring, empowering organisations to determine the credit worthiness of individuals in regions where bureau data is limited or unavailable. For instance, telecommunication network providers often represent great sources of alternative data. User behaviour such as calling patterns and app usage may all be utilised to build a credit profile of an individual, which may then be used as a reference for financial institutions.

Experian has recently inked partnerships with the likes of C88 Financial Technologies and BankBazaar. Both collaborations are envisioned to extend financial accessibility to millions of consumers across Indonesia, Philippines, and India. Moving ahead, the next step for Experian is to work with banks to overhaul client processing, speeding up the process through the use of alternative data.

Experian’s Venture and ‘service for equity’ programs are due to be introduced at a later date. In the meantime, start-ups keen on working with Experian can contact us here.